Multiple Input Multiple Output (MIMO) wireless systems offer merits of combating fading through Diversity, as well as enhance capacity (Spatial Multiplexing) as compared to Single Input Single Output (SISO) systems. Receive signal MIMO detection is a process of determining transmitted symbols sent (transmitted) from different transmit antennas. The receive detection involves separating each of the transmitted data stream optimally from the rest.
An optimal receiver detection scheme is the Maximum Likelihood (ML) scheme (if the noise is AWGN—additive white Gaussian noise), which computes the Euclidean distance between the received signal vector and product of all possible transmitted vectors with an estimated Channel Matrix, H. The complexity of this method however, increases exponentially with modulation order and the number of transmit antennas, making it particularly complex when utilized with MIMO systems.
Existing solutions involve using either a complete implementation of the ML scheme, which results in exponential increase in complexity or implementation of sub-optimal techniques, such as (but not limited to) Zero-Forcing, MMSE (minimum mean square error), Sphere Decoding, QRM, thereby trading performance with complexity. Some receive detection techniques such as Zero-Forcing and MMSE limit the Diversity advantage, though, they are the simplest in terms of implementation complexity.
It is desirable to have apparatuses and methods for receiver signal detection that provide the performance of Maximum Likelihood (ML) receive signal detection but does not require the complexity, particularly when used in MIMO systems.